Publications

Although Life Cycle Assessment (LCA) is a key method for evaluating circular
manufacturing, its complexity restricts its integration into operational decision-making
on the shop floor, where daily deviations in resource consumption and waste
generation can accumulate into substantial environmental impacts. Parameterizable
LCAs and environmental surrogate models can bridge this gap by linking key
operational variables to fast environmental impact estimates, enabling decision
support. Yet, a major bottleneck lies in systematically identifying and selecting critical
parameters within interconnected industrial systems. Modelers often struggle to
capture non-obvious interdependencies across process steps, which can lead to overly
simplified models. To address this challenge, this paper presents an adapted Fuzzy
Cognitive Mapping (FCM) approach to identify and prioritize operational variables for
parameterizable LCA and surrogate modeling. The approach uses an interactive, multi-
step method to capture first-order causal relationships from industry experts. To
analyze how effects propagate through the manufacturing system, higher-order indirect
influences are identified via a network analysis model. The resulting multi-order
dependencies are translated into ego-network representations that show how localized
changes propagate through the process chain. The approach is demonstrated in three
industrial use cases from the EU-funded ENCIRCLE project: aluminum recycling and
casting, hot-dip galvanizing, and household appliance refurbishment. By mapping the
interplay of process, digital/AI, and sustainability variables, the adapted FCM approach
supports the exclusion of less relevant factors and highlights critical leverage points.
Ultimately, this methodology links qualitative domain expertise with quantitative
environmental modeling, providing a foundation for selecting parameters to develop
LCA surrogate models.

Access to reliable, interoperable life cycle data is essential for enabling transparent, reproducible and scalable sustainability assessments. As digital product information flows across organisations, sectors and tools, the ability of systems to exchange and interpret data consistently becomes a critical requirement for the emerging Digital Product Passport (DPP). This presentation examines the current landscape of interoperability standards and derives key technical, semantic, syntactic and organisational requirements that can support the development of cross-sectoral, machine-readable and trustworthy sustainability data infrastructures.

Artificial intelligence (AI) can support the transition to a circular economy (CE) at every stage of the value chain. This means AI can help to conserve resources, extend product lifespans and close material loops as far as possible. However, this can only be achieved if the resource savings are not outweighed by AI’s own resource consumption and if rebound effects are avoided – such as increased consumption resulting from new products or services on the market. This policy paper illustrates potential applications through practical examples and provides recommendations for the socially, economically and environmentally sustainable use of AI in seven key areas of the CE.

Life-cycle assessments are rarely applied as often to the operational decision-making in production as to the planning stage, despite the significant cumulative environmental impacts of high-volume processes. The bottleneck here is that an LCA is too complex for daily use, and decision-makers in production are not trained to interpret the results.

For the ENCIRCLE project, parameterised LCAs are conducted, using as parameters operational variables. The parameters are varied and used to train a surrogate model with the output, i.e. the life-cycle impacts. To determine the stopping criterion for the calculations, the CO2e footprint from the LCA calculations is compared with the optimisation potential derived from more accurate LCI results.

The approach is being tested in two industrial use cases: galvanising and aluminium recycling. As the surrogate model’s output is integrated into an Reinforcement Learning AI agent as an optimization objective, it must aggregate all impacts into a single normalized value to form its reward function, a requirement present both in the simulation environment where the agent trains, and the live production line in which it will imminently be deployed.
To this end, the values in the damage categories of ReCiPe 2016 are normalised so that a standard use case corresponds to 100%.

An interactive notebook is used to demonstrate how the surrogate model is generated and ported in Brightway 2.5, using the joblib package.

Achieving a sustainable circular economy necessitates a rethinking of urban metabolism and resource recovery. While organic waste is a critical residual stream, its potential is often undermined by poor sorting quality and avoidable food waste at the household level. This research addresses these challenges by investigating digital gamification as a tool to activate citizens and overcome psychological barriers to consistent circular behavior. Applying the COMB model, we conducted a quasi-experimental field study in two diverse urban quarters using a gamified smart city application. The study integrates longitudinal survey data with objective behavioral measurements from sensor-equipped waste collection vehicles. Beyond sorting purity, we assess the impact of gamified engagement on knowledge, attitudes toward waste avoidance, and the role of neighborhood identification. The results offer guidance for municipal authorities on designing digital tools to foster long-term circular behavior and participation.

Artificial intelligence (AI) can support the transition to a circular economy (CE) at every stage of the value chain. This means AI can help to conserve resources, extend product lifespans and close material loops as far as possible. However, this can only be achieved if the resource savings are not outweighed by AI’s own resource consumption and if rebound effects are avoided – such as increased consumption resulting from new products or services on the market. This policy paper illustrates potential applications through practical examples and provides recommendations for the socially, economically and environmentally sustainable use of AI in seven key areas of the CE.

Technical advances in the field of artificial intelligence (AI) have frequently been the subject of media coverage in recent years. This has also brought into sharp focus the high energy consumption of some large generative AI models. Although many AI applications consume significantly less energy and fewer resources than the well-known large language models such as GPT-5, the same principle applies: the more training data is used to achieve better results, the higher the energy consumption and the carbon footprint.

The expansion of tube ends is often realized by axial forming with a mandrel. Usually, the purpose of this forming is to allow the expanded tube end to be fitted onto another tube end. In order to ensure the tightness of this connection, the expanded tube end is slit so that it can be clamped. Residual stresses are released from the expansion process, among other things, resulting in shape deviations in the form of out-of-roundness. In this case, reworking must be carried out in order to enable joining. The required tightness must be maintained by all means if the tubes are used to conduct fluids. The aim of the research is therefore to minimize the residual stresses caused by expansion. For the suitability of the component, however, it is not the residual stress distribution itself that is important, but the resulting shape after trimming. For this reason, a FE simulation model was created which was used to compare the calculated residual stress distributions and the resulting shape deviations after cutting. The influences of qualitative and quantitative process parameters were analyzed, in particular those of the mandrel geometry. Among other things, different mandrel geometries were examined and optimized which effect a two-stage forming in one stroke. The simulation results are validated with a series of experiments.